Seasonality, promotional events, and/or local events may affect demand of an item. For example, sales of sunscreen may peak during the summer and significantly dropped in the winter. On the other hand, cold/flu medicine may be in greater demand during winter than any other time of the year. Typically, a retail and/or wholesale establishment determines demand of a distribution center (DC) and related stores based on historical information such billings from the DC (i.e., output from the DC). That is, future billings of the related stores are based on past billings from the DC. For example, orders for a particular item may be made based on the number of units shipped out of the DC from last year. However, the historical billing may not provide an accurate assessment to forecast demand of the DC and related stores because the historical billing may include promotional demand. Further, the historical billing may be imprecise because a particular number of an item may have been shipped out previously but stores may not have sold every single unit of that item. In other words, stores may have a surplus of a particular item. For example, a store may be overstock with the item from a prior year, sale, and/or special.
To determine demand for a particular item simply at the store level may provide too small of a sample to accurately determine how many units of the item to order by the DC. Other factors such as current store inventory, multiple store orders, opening of new stores, closing of existing stores, and relocating of existing stores to more profitable locations may be also affect the DC order for the particular item. Therefore, a need exists for improving the effectiveness and efficiency of the process for forecasting demand of a distribution center and stores associated with the distribution center.